In every large scale study, missing data is ubiquitous and inevitable. The overall cost incurred by the lost information is enormous, both financially, as cases are omitted due to incomplete data, and scientifically, as omitting data can lead to biased results. Research statisticians have developed high quality methods for estimating missing data, which can often enable the data analyst to use cases that would otherwise have to be dropped from the study. However, this research is not yet readily available to the practicing analyst, because it is not embedded in software that is both accessible and easy to use. We propose to extend the S-PLUS statistical computing language to allow data analysts to input and manipulate missing data models. We aim to support popular imputation methods, the EM algorithm, Markov Chain Monte Carlo, and multiple imputation methods. Moreover, we will provide an implementation that will permit researchers to experiment more easily with new missing data methods, as the software will be fully extensible.